Amazon Personalize enhances Recommendation Filters with filtering on item metadata
Amazon Personalize uses machine learning technology perfected from over 20 years of recommender systems development at Amazon.com. With Amazon Personalize you are can personalize recommendations for products, videos, music, ebooks, ads, marketing emails, and more, for your users, without any prior machine learning experience.
Amazon Personalize uses machine learning technology perfected from over 20 years of recommender systems development at Amazon.com. With Amazon Personalize you are can personalize recommendations for products, videos, music, ebooks, ads, marketing emails, and more, for your users, without any prior machine learning experience.
Today, we are pleased to announce enhancements to Recommendation Filters in Amazon Personalize, which provide you greater control on recommendations your users receive by allowing you to exclude or include items to recommend based on criteria that you define. For example, when recommending products for your e-retail store you can exclude unavailable items from recommendations; or if you are recommending videos to users you can choose to only recommend premium content if the user is in a particular subscription tier. Customers typically address this today by writing custom code to implement their business rules, customers can now save time and streamline their architectures by using Recommendation Filters in Personalize.
Setting up and using a custom Recommendation Filter is simple. First, you use the Amazon Personalize console or API to create a filter using an Amazon Personalize-specific DSL (Domain Specific Language). Next, you apply this filter while querying for real time recommendations using the GetRecommendations or GetPersonalizedRanking API; or while generating recommendations in batch mode through a batch inference job.
Recommendation Filters in Amazon Personalize are now available in US East (N. Virginia, Ohio), US West (Oregon), Canada (Central), Europe (Ireland),and Asia Pacific (Sydney, Tokyo, Mumbai, Singapore, Seoul).